Exploring Association Rules Mining in E-commerce Website Design
Association rules mining is a crucial data mining technique for discovering local patterns, particularly in customer buying behaviors. This showcase delves into the utilization of web usage mining to enhance the design of an e-commerce website, OrOliveSur.com. Data collection, pre-processing, and features analysis provide valuable insights into improving user experience and increasing customer engagement.
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CS548 Spring 2016 Association Rules Showcase by Shijie Jiang, Yuting Liang and Zheng Nie Showcasing work by C.J. Carmona, S. Ram rez-Gallego, F. Torres, E. Bernal, M.J. del Jesus, S. Garc a on Web usage mining to improve the design of an e-commerce website: OrOliveSur.com
Sources [1] Carmona, Crist bal J., Sergio Ram rez-Gallego, F. Torres, E. Bernal, Mar a Jos del Jes s, and Salvador Garc a, "Web usage mining to improve the design of an e-commerce website: OrOliveSur. com. , Expert Systems with Applications, vol. 39, no. 12, pp. 11243-11249, Sep. 2012. URL: http://www.sciencedirect.com/science/article/pii/S0957417412005696 [2] S. Rao, R. Gupta, Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm , International Journal of Computer Science And Technology, vol. 3, Issue 1, pp. 489-493, Mar. 2012. URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.228.6638 [3] Herrera, Franciso, Crist bal Jos Carmona, Pedro Gonz lez, and Mar a Jos Del Jesus, "An overview on subgroup discovery: foundations and applications," Knowledge and information systems, vol. 29, no. 3, pp. 495-525, Dec. 2011. URL: http://link.springer.com/article/10.1007%2Fs10115-010-0356-2
Web Mining Personalization: Recommendation Systems Pre-fetching and Caching: Improve the performance of servers Usability of a website: Provide guidelines for designing of Web E-commerce: Customer Relationship Management
Data Collection and Pre-processing Dataset source: Google analytics , 2011, 1stJan to 31stDec Filter: Bounce rate < 100% (only collected visits where users have visited the website for more than one second) Number of Data Instances: 8,832
Features (12 attributes) Browser: Internet Explorer Time on site time spent on sites Visitor Type: new(N) or Visits: # of visits returning(R) visitor Unique page views: # of unique page views Keyword: Olive oil, Iberian Page views per visit: page views/visits Source: Direct(D), Engine(E) Unique page views per visit: unique page New visits: # of new visits views/visits Page views: page views for users Time per page view: time spent /page view
Association Rules Association rules mining is one of the major data mining techniques, and perhaps the most common form of local-pattern discovery. It is likely to be useful in applications that use similarity in customer buying behavior in order to make peer recommendations [1]. Figure taken from amazon.com
Association Rules An association rule describes relations between items and often takes the form: X-->Y. For example: {butter, bread} {milk}, which means that if butter and bread are bought, then customers are likely to also buy milk. Association rule generation is usually split up into two separate steps: 1. A minimum support threshold is applied to find all frequent item-sets in a database. 2. A minimum confidence constraint is applied to these frequent item- sets in order to form rules.
Measures of Interestingness Items: {milk, bread, butter, beer, diapers} Support({milk, bread, butter}) T: the number of transactions = S(milk,bread,butter)/T= =0.2 Confidence({butter, bread}-->{milk}) S(X): the number of transactions that contain item X = Support({butter, bread, milk})/ Support({butter,bread}) = 0.2/0.2=1 Lift(butter, bread}-->{milk}) = Confidence({butter, bread}-->{milk}) /Support({milk}) =1/0.4= 2.5
Apriori Algorithm Minimum Support=0.3 1 itemset Pairs (2 itemsets) Item Support Item Support Browser=Chrome 0.2 {Visitor Type=N, Source=D} 0.3 {Visitor Type=N, keyword=olive oil} Visitor Type=N 0.5 0.4 Source=D 0.7 {Visitor Type=N, visits>=1.5} 0.1 keyword =olive oil 0.6 {Source=D, keyword=olive oil} 0.3 Unique page views <=1 {Source=D, Visits>=1.5} 0.2 0.1 {keyword=olive oil, Visits>=1.5} 0.3 Visits>=1.5 0.4
Apriori Algorithm Pairs (2 itemsets) Triplets (3 itemsets) Item Support {Visitor Type=N, Source=D} 0.3 Item Support {Visitor Type=N, keyword=olive oil} 0.4 {Visitor Type=N, visits>=1.5} 0.1 {Visitor Type=N , Source=D, keyword=olive oil } 0.4 {Source=D, keyword=olive oil} 0.3 {Source=D, Visits>=1.5} 0.2 {keyword=olive oil, Visits>=1.5} 0.3
Mining Results and Analysis - Association Rules Apriori Algorithm
Mining Results and Analysis - Association Rules Potential customers Need to find the keywords they used for searching Cluster A and D are confirmed
Mining Results and Analysis - Clustering Potential customers Need to find the keywords they used for searching Majority are IE and Chrome users
Mining Results and Analysis - Association Rules Most accesses performed with keyword olive oil Review the position of other Iberian products
Mining Results and Analysis - Association Rules Be careful with changes in website design because those changes could confuse habitual clients
Conclusions Find out demands of the potential customers who use search engine with unidentified keywords. Improve the position of other Iberian products because the majority accesses were searching for olive oil. Improve the images and descriptions in OrOliveSur.com displayed in IE and Chrome as the majority of users use IE and Chrome to visit the website. Be careful with changes in website design because those changes could confuse habitual clients.
